Related papers: A Survey of Learning-based Automated Program Repai…
In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients---the code…
Background: Testing and validation of the semantic correctness of patches provided by tools for Automated Program Repairs (APR) has received a lot of attention. Yet, the eventual acceptance or rejection of suggested patches for real world…
Automated Program Repair (APR) has advanced rapidly with Large Language Models (LLMs), but most existing methods remain computationally expensive, and focused on a small set of languages. Ruby, despite its widespread use in web development…
Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their systems to ensure that the expected behavior is delivered.…
Automated program repair (APR) techniques have achieved conspicuous progress, and are now capable of producing genuinely correct fixes in scenarios that were well beyond their capabilities only a few years ago. Nevertheless, even when an…
Large Language Models (LLMs) have recently shown strong potential in automatic program repair (APR), especially in repository-level settings where the goal is to generate patches based on natural language issue descriptions, large…
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evaluated using static benchmarks such as Defects4J and…
Large language models (LLMs) have recently demonstrated strong potential for automated program repair (APR). However, existing LLM-based techniques primarily rely on coarse-grained external feedback (e.g.,test results) to guide iterative…
[...] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortunately, the effectiveness of these advanced techniques in the…
A long-standing open challenge for automated program repair is the overfitting problem, which is caused by having insufficient or incomplete specifications to validate whether a generated patch is correct or not. Most available repair…
Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience. This paper introduces RepairAgent, the first work to address the program repair challenge…
(Note: This work is a preprint.) Static analysis (SA) tools produce many diagnostic alerts indicating that source code in C or C++ may be defective and potentially vulnerable to security exploits. Many of these alerts are false positives.…
The automated program repair field has attracted substantial interest over the years, but despite significant research efforts, creating a system that works well for complex semantic bugs such as security vulnerabilities has proven…
Large Language Models (LLMs) have been gaining increasing attention and demonstrated promising performance across a variety of Software Engineering (SE) tasks, such as Automated Program Repair (APR), code summarization, and code completion.…
Debugging software remains a labor-intensive and time-consuming process despite advances in testing and verification. Learning-based automated program repair (APR) has shown promise in reducing the effort of manually fixing bugs. However,…
Automated program repair (APR) is a technology that identifies and repairs bugs automatically. However, repairing multi-chunk bugs remains a long-standing and challenging problem because an APR technique must consider dependencies and then…
LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well known datasets like Defects4J, raising questions about their…
Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we…
Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce…
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and…